학술논문

A Deep Learning-Based Real-time Seizure Detection System
Document Type
Conference
Source
2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Signal Processing in Medicine and Biology Symposium (SPMB), 2020 IEEE. :1-6 Dec, 2020
Subject
Bioengineering
Signal Processing and Analysis
Sensitivity
Tools
Signal processing
Electroencephalography
Real-time systems
Task analysis
Monitoring
Language
ISSN
2473-716X
Abstract
Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5].